India's population is aging rapidly: by 2036, the number of people over 60 will reach 230 million. Yet almost all existing norms for cognitive and physiological aging come from Caucasian populations. The Indian brain is on average smaller in volume, and the prevalence of diabetes and hypertension among the elderly here reaches 68%. Existing Indian studies focus on pathology, not on norms. This work fills that gap by collecting a healthy-aging reference standard in a relatively homogeneous group of residents of Telangana and Andhra Pradesh.
The study is recruiting 300 healthy volunteers: 100 people each in the 19–35, 40–55, and 60–75 age groups, with equal numbers of men and women. The exclusion criteria are strict: no chronic diseases, no medication use for longer than three months, no psychiatric diagnoses, and no contraindications to MRI. Participants undergo a 45-minute scan on a 3T Siemens Magnetom Lumina scanner, donate 8 mL of blood, provide a lock of hair from the crown of the head, and complete a battery of cognitive tests. Blood is separated into plasma and mononuclear cells, from which DNA and RNA will later be extracted for epigenetic and transcriptomic analysis. Hair cortisol is extracted with methanol and measured by enzyme immunoassay. Heart rate variability is recorded for 5 minutes in a seated position.
The paper describes a protocol, so there are no results yet. Data collection began in 2023 and will continue until the end of 2026. The planned comparisons include gray matter volume, cortical thickness, diffusion MRI metrics, and functional connectivity between age groups. Separately, hair cortisol levels, inflammatory markers, and plasma metabolites will be compared with cognitive measures. The plan is to build normative profiles for each age range and assess individual variability in aging.
This work differs from neighboring aging studies in India in that it excludes chronic diseases and focuses on norms rather than pathology. Most neighboring projects (LASI, dementia cohort studies) recruit participants over 45 and look for risk factors for cognitive impairment. Here, data are collected from young adults and middle-aged people, which will make it possible to trace trajectories of change across the entire adult lifespan. In addition, the study combines molecular, neuroimaging, and psychosocial measurements in a single cohort, something that had not been done in India before.
The collected data will make it possible to train models that predict biological age from multimodal features. A deviation of predicted age from chronological age could become an early marker of accelerated aging. In the future, this dataset could serve as the basis for developing screening tools to identify people at high risk of dementia long before symptoms appear.
The results will affect geriatrics, neurology, and psychiatry in India by providing local reference values for diagnosis. Health policymakers will gain data for planning active longevity programs.
Complete recruitment of the 300 participants by the end of 2026 and conduct an initial analysis of age-related differences. Then integrate omics data with neuroimaging and cognitive tests to identify molecular subgroups of aging.
The study is directly connected to the unresolved problem of defining biological age and how it differs from chronological age. The Indian population still lacks reliable biomarkers of healthy aging, which complicates early diagnosis of neurodegenerative diseases.
Hair cortisol offers a glimpse 3–4 months into the past: each centimeter of hair stores roughly 2–3 weeks of hormonal history. The three inches cut from participants correspond to one season of life.
Not published on arXiv. Here are its page, the PDF and the formalization: you can check it yourself, not only read our account.